Field of the Invention
The present invention concerns a method for reconstructing medical image data. The present invention further concerns a system configured to perform such a method, and a non-transitory, computer-readable data storage medium encoded with programming instructions that cause a computer or computer system to execute such a method.
Description of the Prior Art
The generation of medical image data on the basis of raw data acquired by a medical imaging device, such as a magnetic resonance apparatus, is computationally intensive and imposes heavy demands on the computers that perform such calculations. The aim of new acquisition and reconstruction strategies, such as iterative reconstruction for example, is to acquire smaller quantities of raw data than has traditionally been the case and to achieve an equal or better quality of image data with the use of new reconstruction algorithms. Alternatively, this also enables the quality of the image data to be increased while the quantity of the acquired raw data remains the same. The advantages of acquiring a smaller volume of data are different for different imaging modalities. In computed tomography, the applied energy dose can correlate with the data volume, in magnetic resonance tomography the acquisition time. In both cases, a reduction in the amount of data is advantageous. In contrast to the conventional reconstruction algorithms, these new algorithms are orders of magnitude more computationally intensive and place considerably higher demands on the working memories of the computers that perform said algorithms. It is desirable to complete the reconstruction of the image data within the shortest possible length of time and to provide said image data to an image viewing module in order to facilitate a smooth clinical workflow. This calls for computers that are capable of satisfying the high demands of the new algorithms.
Commercial medical imaging devices are equipped among other components with a computer that processes the acquired raw medical data of precisely the medical imaging device and usually is also spatially associated with that device. The performance of the computer is dimensioned such that it is capable of running the algorithms required by the medical imaging device on all the acquired raw data. Accordingly, the length of time required for executing the algorithm is precisely determined. Since there are strong variations in both the amount of data and the demand imposed by the algorithms on the processing power of the computer, the capacity of the computer is oftentimes utilized only to a limited extent, or even not at all if, for example, the medical imaging device is not in operation.
It is known that particularly computationally intensive algorithms are not performed on computers that are associated directly with the medical imaging device, but use external computers.
Yarra (ktblock.de/yarra) and Meng et al. (Ultrafast and scalable cone-beam CT reconstruction using MapReduce in a cloud computing environment, in Med. Phys. 2011, 38:6603-6609) provide as one embodiment of the method that different medical imaging devices have one or more shared computers with high computing capacity at their disposal and can access the same as necessary.
A further possibility is to lease computing capacities from commercial service providers (Cloud computing, de.wikipedia.org/wiki/Cloud_Computing).
The use of free capacities of a number of computers can furthermore be controlled by the BOINC framework (boinc.berkeley.edu/). BOINC is used by the SETI@home project (setiathome.ssl.berkeley.edu/), for example.